(LUẬN văn THẠC sĩ) performance analysis of network MIMO systems

VIET NAM NATIONAL UNIVERSITY, HA NOI
UNIVERSITY OF ENGINEERING AND TECHNOLOGY

PERFORMANCE ANALYSIS OF
NETWORK-MIMO SYSTEMS
A THESIS SUBMITTED
IN FULFILMENT OF THE REQUIREMENTS
FOR THE DEGREE OF MASTER OF EECTRICAL ENGINEERING

DUC-TUYEN TA
2010

Supervisor: Dr. Trinh Anh Vu
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ACKNOWLEDGMENTS

First and foremost, I would like to express my gratitude to Dr. Trinh Anh Vu for
being a great mentor and for numerous technical discussions and suggestions that
have found their way into this thesis. I also very thank to all my colleagues at
University of Engineering and Technology, VNU who have contributed greatly to
provide a supportive and collaborative research atmosphere. Many thanks to Phd.
Tran Duc Tan and Dinh Van Phong, with whom I have had opportunities to
collaborate on various subjects.
I would like to sincerely thank my parents for their support, encouragement, and
love throughout my life. This thesis is dedicated to them.

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ABSTRACT

Network MIMO is a means of coordinating and processing the information
gathered from multiple- input multiple- output (MIMO) communication systems
to increase spectral efficiency, robustness, and data rates. These properties make it
a topic of great interest in the near future as the number of wireless users continues
to grow and their individual demands on bandwidth climb. Systems employing
network MIMO capitalize on the fact that inter-cell interference, a major problem
for dense wireless systems, is a superposition of signals. With careful coordination
between receivers (and transmitters), these super-positions can be decoupled and
the information they contain can be utilized.
The goal of this thesis is to investigate the ability of network MIMO
techniques to increase data rates in multi-user indoor wireless networks of various
sizes with various channel schemes. The simulation results also show that
Network MIMO systems can be increase data rates and good through put than
non- networked MIMO systems.

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AUTHOR’S DECLARATION

I declare that the work in this thesis was carried out in
accordance with the Regulations of the University of

Engineering and Technology, VNU. The work is original except
where indicated by special reference in the text and no part of
the thesis has been submitted for any other degree. Any views
expressed in the dissertation are those of the author and do not
necessarily represent those of the University of Engineering,
VNU. The thesis has not been presented to any other university
for examination either in Viet Nam or overseas.

Duc-Tuyen Ta

15 October 2010

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TABLE OF CONTENTS
Page
LIST OF TABLES ................................................................................................ vii
LIST OF FIGURES ............................................................................................. viii
ABBREVIATIONS................................................................................................ xi
CHAPTER 1: INTRODUCTION ......................................................................... 1
1.1 Wireless Communication ......................................................................... 1
1.2 MIMO Techniques .................................................................................... 2
1.3 Network-MIMO systems .......................................................................... 5
1.4 Thesis’s Structure...................................................................................... 5
CHAPTER 2: BASIC MIMO THEORY ............................................................. 7
2.1 Wireless Background ................................................................................ 7
2.2 MIMO Communications .......................................................................... 8

2.2.1 MIMO systems Model .................................................................. 9
2.2.2 Theoretical MIMO Capacity Gains ............................................ 10
2.2.3 Types of MIMO .......................................................................... 12
2.3 Multi-user Communications .................................................................. 12
2.3.1 Limitations of Single-User view ................................................. 13
2.3.2 Multi-User MIMO (MU-MIMO) ................................................ 14
2.4 Multi-cell Communications .................................................................... 18
2.4.1 Limitations of Single-Cell View ................................................. 19
2.4.2 Multi-Cell MIMO ....................................................................... 19
3.1 Background .............................................................................................. 21
3.1.1 Inter-cell Interference .................................................................. 21
3.2 Theory behind Network MIMO ............................................................ 27
3.3 Network-MIMO systems Model ............................................................ 28
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3.3.1 Uplink.......................................................................................... 29
3.3.2 Downlink ..................................................................................... 30
CHAPTER 4: SIMULATION AND RESULTS ................................................ 34
4.1 Simulation Model .................................................................................... 34
4.2 Simulation Diagram ................................................................................ 36
4.3 Simulation Results................................................................................... 39
CHAPTER 5: CONCLUSION ............................................................................ 45
REFERENCES ..................................................................................................... 46

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LIST OF TABLES
Page
Table 1 Power Delay Profile .................................................................................. 35
Table 2 Simulation parameters ............................................................................... 39

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LIST OF FIGURES
Page
Figure 1 MIMO communication from SISO to IA-MIMO (Source:
www.wikipedia.org) ................................................................................................. 4
Figure 2 MIMO channel with M transmit and N receive antennas. The sketched
path, from transmitter and receiver, represent the channel which h11 is the channel
between transmit antenna 1 and receive antenna 1. The transmit and receive signal
are often presented by “black boxes” ....................................................................... 9
Figure 3 From single- to multiuser communications, where all the users in the
coverage area are simultaneously considered in the optimization. The base station
may choose to transmit data to a single or multiple user terminals at once. .......... 14
Figure 4 Illustration of MU-MIMO: Downlink and Uplink .................................. 15
Figure 5 MU-MIMO systems: MIMO Broadcast (Source: www.wikipedia.org) . 16
Figure 6 MU-MIMO systems: MIMO MAC (Source: www.wikipedia.org) ....... 17
Figure 7 Frequency reused in cellular network with the reuse factor is 3 and 7.
Cells of same color are used with same frequency. ............................................... 18
Figure 8 From multi-user to multi cell communication, where all the cells and all
the users in the network are simultaneously considered in optimization. The solid

line marks the useful signals, where the interfering is dashed. .............................. 20
Figure 9 Coordination or Cooperation between all base stations in the wireless
communication network under fast backhaul. The central unit played an central
network controller for control the coodination/cooperation between all the BS. .. 20
Figure 10 Illustration of typical interference between users and access points in a
cell-based wireless system. The left image shows interference in down link and the
right image shows interference in uplink. .............................................................. 22
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Figure 11 Illustration of traditional interference control between users and access
points in a cell-based wireless system. The left image shows down link and the
right image shows uplink........................................................................................ 23
Figure 12 Illustration of MIMO interference control between users and access
points in a cell-based wireless system. The left image shows down link and the
right image shows uplink........................................................................................ 24
Figure 13 Example of a small wireless communication with terminals, AP and the
Central Network Controller. ................................................................................... 25
Figure 14 Network MIMO solution where all the signals are useful, i.e.,
interference is removed .......................................................................................... 25
Figure 15 Conventional vs. Network MIMO average SINR and data rate
improvements. ........................................................................................................ 26
Figure 16 Wireless network with two transmit and two receive antennas
communicating through independent channels ...................................................... 27
Figure 17 Network-MIMO uplink channel: from m-th cell to all of base station.
................................................................................................................................ 29

Figure 18 Network-MIMO downlink channel: from all base station to k-th user in

the m-th cell ............................................................................................................ 31
Figure 19 Block Diagram showing key functions that are to be implemented in
MATLAB simulation ............................................................................................. 37
Figure 20 Simulation environment with 9 cell, each cell include 1 access point and
1 end-user with randomly place. ............................................................................ 40
Figure 21 OFDM Pilot symbol to estimate the channel state information at both
transmitter (AP/user) and receiver (user/AP) side with 3 users. ............................ 41
Figure 22 Compare between real channel and the estimated channel by using pilot
symbol. ................................................................................................................... 42
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Figure 23 Channel estimation between 4-th AP and 1-st User (in the different cell)
and the channel between 1-st AP and 1-st cell (in the same cell). ......................... 43
Figure 24 Comparison between performance of Network-MIMO and non
Network-MIMO communication system with the ranger of Signal-to-Noise Ratio
(SNR) is 10 to 20 dB. ............................................................................................. 43

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ABBREVIATIONS

1G, 2G, 3G, 4G

1st to 4th generations of wireless (phone) networks

BER

Bit Error Rate

CSCG

Circularly Symmetric Complex Gaussian

CSI

Channel State Information

CSIR

Channel State Information at the Receiver

CSIT

Channel State Information at the Transmitter

DPC

Dirty Paper Coding

GSM

Global System for Mobile(originally: Groupe Spéciale Mobile)

IEEE

Institute of Electrical and Electronics Engineers

LOS

Line of Sight

MIMO

Multiple-Input Multiple-Output

MISO

Multiple-Input Single-Output

MMSE

Minimum Mean Square Error

MU-MIMO

Multiuser MIMO

NLOS

Non Line of Sight

OFDM

Orthogonal Frequency Division Multiplexing

OSTBC

Orthogonal Space Time Block Code

PEP

Pairwise Error Probability

RF

Radio Frequency

SDMA

Space Division Multiple Access

SER

Symbol Error Rate

SIMO

Single-Input Multiple-Output

SINR

Signal to Interference and Noise Ratio

SNR

Signal to Noise Ratio

STBC

Space Time Block Code
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STC

Space Time Code

SU-MIMO

Single-User MIMO

WiMAX

Worldwide Interoperability for Microwave Access

WLAN

Wireless Local Area Network

ZF

Zero-Forcing

MSE

Mean Square Error

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CHAPTER 1: INTRODUCTION

Modern wireless networks tend to be interference limited, mainly caused by
their own base stations and mobile terminals. Suppressing interference would thus
result in significant improvements in data rates, capacity, and coverage. Our
studies determined the feasibility of achieving significant performance Network
MIMO (Multiple-Input/Multiple-Output) gains. This led to a proposed solution to
suppress inter-cell interference via phase- coherent coordination and joint spatial
filtering between the base stations.
1.1 Wireless Communication
Wireless communication services are basic features of global civilization, soon
available everywhere and adopted by everyone. The development has been
especially rapid in the last few decades, in which time wireless communications
has taken a leap from being a niche technology towards achieving a status as an
independent growth industry and diverse research area [1].
The history of wireless communication technologies can be traced back
over 140 years, to Maxwell’s theories on electromagnetic waves and Hertz’ later
demonstration of their existence [2]. Marconi’s 1896 invention of wireless
telegraphy

supplied

the

first

useful

application,

enabling

transatlantic

communication services. Then followed radiotelephony, and commercial car
phone services were spreading slowly from the late 1920s [3].
First generation (1G) personal mobile phone systems came in the early
1980s, with user terminals that were expensive and of questionable portability.
However, the introduction of a cellular structure, for base station location and
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frequency reuse, helped control the interference and made the networks more
easily scalable, and the wireless revolution was ignited. The analog 1G networks
were followed by the digital second generation (2G) systems, among which the
GSM, first introduced for regular service in Finland in 1991, is one successful
example.
Third generation (3G) standards were released from 2000, aiming for

unified global roaming, more users and higher data rates. However, the actual
deployment of networks was long delayed by enormous spectrum licensing fees
and a lack of industry incentive. The fourth generation (4G) of wireless networks,
also known as Beyond 3G, notably include implementations of the WiMAX and
the Long-Term Evolution (LTE) standards [4].
For years, there is an on-going shift in end-user mobile communications
service. The future of wireless communication is multimedia, which includes
image, video, and local area network applications; with the data transmission rate
more than 1000 times faster than that of the present systems. However, the
physical limits imposed by the mobile radio channel cause performance
degradation and make it very difficult to achieve high bit rate at low error rate
over the time dispersive wireless channels. Another key limitation is co-channel
interference (CCI) which can also significantly decrease the capacity of wireless
and personal communications systems.
1.2 MIMO Techniques

As presented in Section 1, future wireless communication networks will need to
support extremely high data rates in order to meet the rapidly growing demand for
broadband applications. Existing wireless communication technologies cannot

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efficiently support broadband data rates, due to their sensitivity to fading. Multiple
antennas have recently emerged as a key technology in wireless communication
systems for increasing both data rates and system performance.
The benefits of exploiting Multiple-Input-Multiple-Output (MIMO) may be
categorized by the following [6]:

Array gain
Array gain refers to the average increase in the SNR at the receiver that arises
from the coherent combining effect of multiple antennas at the receiver or
transmitter or both. The average increase in signal power at the receiver is
proportional to the number of receive antennas.
Diversity gain
Signal power in a wireless channel fluctuates. When the signal power drops
significantly, the channel is said to be in a fade. Diversity is used in wireless
channels to combat fading. Utilization of diversity in MIMO channels requires
antenna diversity at both receive and transmit side. The diversity order is equal to
the product of the number of transmit and receive antennas, if the channel between
each transmit-receive antenna pair fades independently.
Spatial multiplexing (SM)
SM offers a linear (in the number of transmit-receive antenna pairs or min (Mt, Mr)
increase in the transmission rate for the same bandwidth and with no additional
power consumption.
Interference reduction
Co-channel interference arises due to frequency reuse in wireless channels. When
multiple antennas are used, the difference between the spatial signatures of the
desired signal and co-channel signals can be exploited to reduce the interference.
This operation is done at the receiver side.
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Figure 1 MIMO communication from SISO to IA-MIMO (Source: www.wikipedia.org)

In addition, we will increase system performance or reduce cost by apply some
enhancement techniques to MIMO communication systems. These can be

categorized into two groups: evolutionary and revolutionary approaches.
•

Evolutionary approaches:
1. Use an existing techniques with enhanced PHY capabilities, perhaps
a 16×16 array configuration.
2. Use new MIMO algorithms such as pre-coding or multi-user
scheduling at the transmitter.

•

Revolutionary approaches: developing the fundamentally of new MIMO
concepts.

Based on the literature, we summarize a number of advanced MIMO techniques
that leverage multiple users as seen in Fig 1:
•

Cross-layer MIMO: Scheduling, etc.

•

Advanced decoding MIMO: Multi-user detection such as MLD.

•

Beamforming and SDMA: widely known multi-user MIMO (MU-MIMO)
scheme.
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•

Infrared/Non-infrared network optimization.

•

Network MIMO (Net-MIMO).

•

Cognitive MIMO based on intelligent techniques.

•

Cooperative/competitive MIMO.

•

Cooperation: DPC, Wyner-Ziv, etc.

•

Competitive: Game theory, autonomous packets, implicit MAC fairness.

•

etc

1.3 Network-MIMO systems

Network MIMO is a MIMO communication scheme, which falls within the family
of techniques that use cooperation in a MIMO systems to increase system
performance. More specifically, network MIMO is a family of techniques whereby
each end user in a wireless access network is served not just by multiple antennas
but also by multiple access points [8]. This allows users similar performance
increases to those seen in other MIMO processing methods but achieves it by
taking advantage of the already existing infrastructure in any multi-point access
network.
For example, an indoor wireless system for a small business would have
several access points (AP). These access points would all be connected through a
wired grid to a central router and then to the internet via an ISP. Taking advantage
of the fact, these access points are all connected, network MIMO could be used to
coordinate the transmission and reception of data without needing to add
additional antennas to local access points.
1.4 Thesis’s Structure

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In general terms, this thesis focuses on performance analysis of network MIMO
systems. Because Network-MIMO is an enhancement model of the original
MIMO systems, we first analysis the theoretical of MIMO techniques in Chapter
2. That is the basic knowledge to work with Network-MIMO in the next chapters.
In Chapter 3, we consider a Network-MIMO systems where two or more
AP served each end-user to achieve high system performance while also reduces

the system interference.
Chapter 4 presented the simulation model and simulation results of a
Network MIMO systems using Matlab. The model simulates an indoor wireless
access system with multiple Access Point (AP) and multiple End-User. For
simplicity, we assumed that the MIMO link is created only by the way of multiple
wireless access. The simulation results show that Network MIMO systems can be
archive high system performance than the non Network-MIMO systems.
Finally, we have some conclusion and discussion about Network-MIMO
systems in Chapter 5.

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CHAPTER 2: BASIC MIMO THEORY

Future wireless communication networks will need to support extremely
high data rates in order to meet the rapidly growing demand for broadband
applications such as high quality audio and video. Existing wireless
communication technologies cannot efficiently support broadband data rates, due
to their sensitivity to fading. Multiple-input multiple-output (MIMO) is a key
technique for increasing both data rates and system performance. It can increase
data throughput and link range without bandwidth or transmit power expansion.
2.1 Wireless Background
A simple wireless communication system consists of a transmitter and a receiver,
both equipped with a single antenna, transmitting information-carrying
electromagnetic waves over space. The transmit antenna provides the input to the
wireless channel, and the output is picked up by the receive antenna, thus, forming
a Single-Input Single-Output (SISO) system.

In this thesis, communications is assumed to take place between a
stationary access point (AP) or base station (BS) and a mobile user terminal (MS).
The BS transmits data to the user terminal on the downlink, while the reverse
direction is the uplink. With a multiple base stations network, these are often
assumed to be connected by a wired or wireless backbone network, offering highrate inter-base communications.
The wireless communications medium is space, and so a system’s
characteristics are highly dependent on the local propagation environments
formed by natural and manmade structures, such as mountains, foliage, buildings,
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and large vehicles. Flat and rural areas offer free space conditions, under which a
transmitted signal will reach the destination only via the direct Line-Of-Sight
(LOS) path. Non Line-Of-Sight (NLOS) conditions occur when the direct path is
blocked, which is common in cities and suburban areas, but which may also be
caused by a countryside hill.
Propagation over space is additive in nature, which makes wireless
communications susceptible to crosstalk between same-frequency signals, so
called co-channel interference (CCI). If the desired and the interfering signal are
received with comparable powers, the desired signal may well be impossible to
retrieve from the new, sum signal.
2.2 MIMO Communications
In wireless communication, multiple input multiple output (MIMO) technology is
the use of multiple antennas in both transmitter and receiver. It has attracted
attention in modern wireless communications, because it offers significant
increases in data throughput and link range without additional bandwidth or
transmit power by higher spectral efficiency (more bits per second per hertz of
bandwidth) and link reliability or diversity (reduced fading). Because of these

properties, MIMO is an important part of modern wireless communication
standards such as IEEE 802.11n, 3GPP Long Term Evolution (LTE), 4G, and
WiMax.

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Figure 2 MIMO channel with M transmit and N receive antennas. The sketched path, from transmitter and
receiver, represent the channel which h11 is the channel between transmit antenna 1 and receive antenna 1. The
transmit and receive signal are often presented by “black boxes”

2.2.1 MIMO systems Model
We consider a MIMO systems with a transmit array of MT antennas and a receive
array of MR antennas. The block diagram of such a system is shown in the Fig 2.
The transmitted matrix is an [M, 1] column matrix S where Si is the 𝑖𝑖𝑡𝑡ℎ
component, transmitted from antenna i, and of the form:
𝑆𝑆 = [𝑆𝑆1 , 𝑆𝑆2 , … , 𝑆𝑆𝑀𝑀 ]𝑇𝑇

Where ( ) T denotes the transpose matrix

For simplicity, we consider the channel is a Gaussian channel such that the
elements of S are considered to independent identically distributed (i.i.d)
variables. Assume that the channel state information (CSI) is known at receiver
but unknown at the transmitter side and the signals transmitted from each antenna
have equal powers of Es/M with Es is the power of transmitted signal.
The channel matrix can be given by:

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ℎ11 ℎ12
ℎ ℎ
𝐻𝐻 = � 21 22
⋮
ℎ1𝑀𝑀 ℎ𝑀𝑀2

…
⋯
⋱
⋯

ℎ1𝑁𝑁
ℎ2𝑁𝑁
�
⋮
ℎ𝑀𝑀𝑀𝑀

The noise at the receiver is another column matrix of size [N, 1], denoted by w:
𝑤𝑤 = [𝑤𝑤1 , 𝑤𝑤2 , … , 𝑤𝑤𝑁𝑁 ]𝑇𝑇

So the receiver vector is [N, 1] vector that satisfied:

𝑅𝑅 [𝑚𝑚] = 𝐻𝐻. 𝑆𝑆[𝑚𝑚] + 𝑤𝑤[𝑚𝑚]

[2-1]

Where m is a real number from 1 to N.

2.2.2 Theoretical MIMO Capacity Gains
According to Shannon capacity of wireless channels, given a single channel
corrupted by an additive white Gaussian noise at a level of SNR, the capacity is:
𝐵𝐵𝐵𝐵𝐵𝐵
�
𝐶𝐶 = 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 [1 + 𝑆𝑆𝑆𝑆𝑆𝑆] �
𝐻𝐻𝐻𝐻
Where: C is the Shannon limits on channel capacity
SNR is signal-to-noise ratio
B is bandwidth of channel.
In the practical case of time-varying and randomly fading wireless channel, the
capacity can be written as:

𝐵𝐵𝐵𝐵𝐵𝐵
�
𝐶𝐶 = 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 [1 + 𝑆𝑆𝑆𝑆𝑆𝑆. |𝐻𝐻 |2 ] �
𝐻𝐻𝐻𝐻

[2-2]

Where H is the 1x1 unit-power complex matrix Gaussian amplitude of the
channel. Moreover, it has been noticed that the capacity is very small due to fading
events [6].
Form the capacity of SISO system; we can calculate the theoretical capacity gain
of MIMO communication system in two cases:
Same signal transmitted by each antenna
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In this case, the MIMO systems can be view in effect as a combination of the
Single Input Multiple Output (SIMO) and Multiple Input Single Output (MISO)
channels. The corresponding SNR of MIMO systems is:
𝑁𝑁 2 . 𝑀𝑀2 . 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝑎𝑎 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝
𝑆𝑆𝑆𝑆𝑆𝑆 ≈
= 𝑀𝑀. 𝑁𝑁. 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆
𝑁𝑁. 𝑀𝑀. (𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛)

[2-3]

Therefore, the capacity of MIMO channels in this case is:
𝐵𝐵𝐵𝐵𝐵𝐵
�
𝐶𝐶 = 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 [1 + 𝑀𝑀. 𝑁𝑁. 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 ] �
𝐻𝐻𝐻𝐻

[2-4]

Thus, we can see that the channel capacity for the MIMO systems is higher than
that of SIMO and MIMO systems.
From Equation [2-4], we can see that the capacity is increasing inside the log
function. This means that trying to increase the data rate by simply transmitting
more power is extremely costly.
Different signal transmitted by each antenna
The big idea in MIMO is that we can send different signals using the same
bandwidth and still be able to decode correctly at the receiver. Thus, it like that we
are creating a channel for each one of the transmitters. The capacity of each one of

these channels is roughly equal to:
𝐶𝐶 = 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 �1 +

𝑁𝑁
𝐵𝐵𝐵𝐵𝐵𝐵
�
. 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 � �
𝑀𝑀
𝐻𝐻𝐻𝐻

[2-5]

However, we have M of these channels, so the total capacity of the system is:
𝐵𝐵𝐵𝐵𝐵𝐵
𝑁𝑁
�
𝐶𝐶 = 𝑀𝑀. 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 �1 + . 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 � �
𝐻𝐻𝐻𝐻
𝑀𝑀
Assume𝑁𝑁 ≥ 𝑀𝑀, the capacity of MIMO channels is roughly equal to:
𝑁𝑁
𝐵𝐵𝐵𝐵𝐵𝐵
�
𝐶𝐶 = 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 �1 + . 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 � �
𝑀𝑀
𝐻𝐻𝐻𝐻

[2-6]

Thus, we can get linear increase in capacity of the MIMO channels with respect to

the number of transmitting antennas. Therefore, the key principle at work here is
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that it is more beneficial to transmit data using many different low-powered
channels than using one single, high-powered channel.
In the practical case of time varying and randomly fading wireless channel, it
shown that the capacity of M x N MIMO systems is [6]:
𝑆𝑆𝑆𝑆𝑆𝑆
𝐵𝐵𝐵𝐵𝐵𝐵
�
𝐶𝐶 = 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 �𝑑𝑑𝑑𝑑𝑑𝑑 �𝐼𝐼𝑁𝑁 +
. 𝐻𝐻𝐻𝐻 ∗ �� �
𝑀𝑀
𝐻𝐻𝐻𝐻

[2-7]

We can see that the advantage of MIMO systems is significant in capacity. As an

example, for a system which 𝑀𝑀 = 𝑁𝑁 and 𝐻𝐻𝐻𝐻 ∗ /𝑀𝑀 → 𝐼𝐼𝑁𝑁
𝐵𝐵𝐵𝐵𝐵𝐵
�
𝐶𝐶 = 𝑀𝑀. 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 [1 + 𝑆𝑆𝑆𝑆𝑆𝑆] �
𝐻𝐻𝐻𝐻

Therefore, the capacity increases linearly with the number of transmit
antennas.

2.2.3 Types of MIMO
MIMO can be categorized into three main categories: pre-coding, spatial
multiplexing, and diversity coding. Pre-coding is multi-layer beamforming in a
narrow sense or all spatial processing at the transmitter in a wide-sense. In (singlelayer) beamforming, the same signal is emitted from each of the transmit antennas
with appropriate phase (and sometimes gain) weighting such that the signal power
is maximized at the receiver input. Spatial multiplexing requires MIMO antenna
configuration. Diversity Coding techniques are used when there is no channel
knowledge (channel state information) at the transmitter.
2.3 Multi-user Communications
There is a shifting trend in research and industry in wireless communication from
single-user (SU) to multiuser (MU), which, in the prevalent cellular network
structure, expands the optimization domain to the entire cell. The multiple antenna
base station and the single or multiple-antenna user terminals form a generalized
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MIMO systems, and approaches for this scenario are referred to as MU-MIMO
communications. Gesbert et al. [7] give a recent overview of the MU-MIMO
paradigm shift, so named because the single- and the multiuser views are
essentially different.
2.3.1 Limitations of Single-User view
The above MIMO schemes and analysis consider a single link between a
transmitter and a receiver, often referring to the single-user scenario when this
link is between a base station and a user terminal. The single MIMO can be seen
as point-to-point MIMO communication. This has some limitations: this focus
neglects lessons learned from information theory, the demands and conditions of
other users, and the presence of co-channel interference (CCI).

First, existing information theoretic results advocate the use of nonorthogonal multiple-access schemes, where multiple, simultaneous users share a
common spectral resource, but are separated in the spatial domain.
Second, disregarding the other users may limit the performance by keeping
a certain single-user connection, even when the channel conditions are
unfavorable.
Third, neglecting the interference makes us overly optimistic on behalf of
the MIMO performance, as the above capacity results are only achievable for
idealized, interference-free transmissions. With no knowledge about the channel,
the transmitter and receiver are unable to mitigate it, and will simply treat is as
noise. Increasing degrees of CSI at the receiver enables more techniques that are
sophisticated.
Nowadays, we can see the shifting trend from single-user (SU) MIMO to
multi-user (MU) MIMO communications, which, in the prevalent cellular network
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